continuous-learning

name	continuous-learning
description	Automatically extract reusable patterns from Gemini CLI sessions and save them as learned skills for future use.

Continuous Learning Skill

Automatically evaluates Gemini CLI sessions on end to extract reusable patterns that can be saved as learned skills.

How It Works

This skill runs as a Stop hook at the end of each session:

1. Session Evaluation: Checks if session has enough messages (default: 10+)
2. Pattern Detection: Identifies extractable patterns from the session
3. Skill Extraction: Saves useful patterns to ~/.gemini/skills/learned/

Configuration

Edit config.json to customize:

{
  "min_session_length": 10,
  "extraction_threshold": "medium",
  "auto_approve": false,
  "learned_skills_path": "~/.gemini/skills/learned/",
  "patterns_to_detect": [
    "error_resolution",
    "user_corrections",
    "workarounds",
    "debugging_techniques",
    "project_specific"
  ],
  "ignore_patterns": [
    "simple_typos",
    "one_time_fixes",
    "external_api_issues"
  ]
}

Pattern Types

Pattern	Description
error_resolution	How specific errors were resolved
user_corrections	Patterns from user corrections
workarounds	Solutions to framework/library quirks
debugging_techniques	Effective debugging approaches
project_specific	Project-specific conventions

Hook Setup

Add to your ~/.gemini/settings.json:

{
  "hooks": {
    "Stop": [{
      "matcher": "*",
      "hooks": [{
        "type": "command",
        "command": "~/.gemini/skills/continuous-learning/evaluate-session.sh"
      }]
    }]
  }
}

Why Stop Hook?

• Lightweight: Runs once at session end
• Non-blocking: Doesn't add latency to every message
• Complete context: Has access to full session transcript

Related

• The Longform Guide - Section on continuous learning
• /egc-learn command - Manual pattern extraction mid-session

---

Comparison Notes (Research: Jan 2025)

vs Homunculus (github.com/humanplane/homunculus)

Homunculus v2 takes a more sophisticated approach:

Feature	Our Approach	Homunculus v2
Observation	Stop hook (end of session)	PreToolUse/PostToolUse hooks (100% reliable)
Analysis	Main context	Background agent (Haiku)
Granularity	Full skills	Atomic "instincts"
Confidence	None	0.3-0.9 weighted
Evolution	Direct to skill	Instincts → cluster → skill/command/agent
Sharing	None	Export/import instincts

Key insight from homunculus:

"v1 relied on skills to observe. Skills are probabilistic—they fire ~50-80% of the time. v2 uses hooks for observation (100% reliable) and instincts as the atomic unit of learned behavior."

Potential v2 Enhancements

1. Instinct-based learning - Smaller, atomic behaviors with confidence scoring
2. Background observer - Haiku agent analyzing in parallel
3. Confidence decay - Instincts lose confidence if contradicted
4. Domain tagging - code-style, testing, git, debugging, etc.
5. Evolution path - Cluster related instincts into skills/commands

See: /Users/affoon/Documents/tasks/12-continuous-learning-v2.md for full spec.